Abstract Prescriptive process monitoring is a family of techniques to optimize the performance business by triggering interventions at runtime. Existing prescriptive assume that number may be triggered unbounded. In practice, though, consume resources with finite capacity. For example, in loan origination process, an intervention consist preparing alternative offer increase applicant’s chances ...

Causal broadcasting has been introduced to reduce the asynchrony of communication channels inside groups of processes. It states that if two broadcast messages are causally related by the happened-before relation, these messages are delivered in their sending order to each process of the group. Even though protocols implementing causal broadcasting do not add control messages, they suuer from t...

One of the most fundamental problems in causal inference is the estimation of a causal effect when treatment and outcome are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest “weak” pa...

In large-scale chemical processes, disturbances can easily propagate through the process units and thereby adversely affect the overall process performance. In recent years, causal analysis has played a key role in the diagnosis of plant-wide disturbances. Causal analysis enables to identify the propagation path of the disturbance and thereby disclose the root cause. Data-driven causal analysis...

A wide variety of knowledge acquisition methods exist for conventional knowledge types such as production rule, semantic knowledge, etc. However, need for causal knowledge acquisition has not been stressed in the expert systems fields. The objectives of this paper are to (1) suggest a causal knowledge acquisition process and (2) investigate the causal knowledge-based inference process. FCM (Fuz...

Investigating the role of causal order in quantum mechanics has recently revealed that the causal relations of events may not be a priori well defined in quantum theory. Although this has triggered a growing interest on the theoretical side, creating processes without a causal order is an experimental task. We report the first decisive demonstration of a process with an indefinite causal order....

We compare three notions of knowledge in concurrent system: memoryless knowledge, knowledge of perfect recall, and causal knowledge. Memoryless knowledge is based only on the current state of a process, knowledge of perfect recall can take into account the local history of a process, and causal knowledge depends on the causal past of a process, which comprises the information a process can obta...

The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyc...

This paper argues that the current way in which the undergraduate introductory econometrics course is taught is neither inline with current empirical practice nor very intuitive. It proposes a shift in focus of the course on causal inference using the Roy-Rubin Causal Model (RRCM). A second theme of the paper is the suggestion to use random regressors from the start to improve the ability of st...

In the recent philosophical literature, two questions have arisen concerning the status of natural selection: 1) Is it a population-level phenomenon, or is it an organism-level phenomenon? 2) Is it a causal process, or is it a purely statistical summary of lower-level processes? In an earlier work (Millstein 2006), I argue that natural selection should be understood as a population-level causal...